Human Genetics in Medicine
Pharmacogenomics
No patient reacts in precisely the same way when dosed with the same
drug. Some will display dramatic differences when treated with the same drug
for the same condition. If we could predict which patients were going to react
badly from a study of their genetic make-up then modifications could be made to
the drugs, or alternatives prescribed before the adverse events.
In a new field called pharmacogenomics of many different genes drug behavior
may be predicted. We do this by being able to define individual single
nucleotide polymorphisms (SNP) that predict a variable response to the
particular drug. The hope for the future is that we will be able to provide
treatment that is personalized. We must not lose sight, however, of the
possibility that this personal genetic information will be misused, and
considerations of safeguards should be at the forefront of plans to utilize
this approach wisely.
The best-known examples of the potential of pharmacogenomics approach
are related to single gene traits that affect drug metabolism. It is not only
variations in drug metabolism that are observed; there is a growing collection
of polymorphisms within the genes that encodes proteins involved in
transporting and targeting drugs. Most of those that have been found are the
ones that are easy to identify because they are associated with single genes
and clearly recognizable effects. This is not how many drugs work, however as
multiple genes may be involved in determining the outcome of the treatment.
This has led to genome-wide approaches to identify genes that determine variant
drug response.
For example, between family response to the antihypertensive drug
debrisoquine were used to identify the CRY2D6 gene in the action of the drug
and polymorphisms identified that were responsible for this variation. The gene
has been shown to be important in the metabolism of around 20 % of described
drugs, including sparteine and propafenone, both anti-arrhythmic drug,
amitriptyline, an antidepressant, and codeine, an analgesic, and this knowledge
could be utilized to benefit individuals.
Even where single genes variant appears to have a strong effect on drug
action, much of the variation in patient response remains unexplained by the
polymorphism alone the reason remains unexplained by the polymorphism alone.
The response for this at]re that there may be many other polymorphisms within
genes that are important in cellular pathways that are involved in the
interaction between the drug and its subsequent effect. It may not only affect
the genes itself, but there may be polymorphisms within, for example, the
promoter and enhancer regions that affect the expression of the genes.
Future studies are likely to identify polymorphism that interact with
each other in different ways. For example, cytochrome P450 enzymes, including
CYP3A5 are important to the metabolism of many drugs and are a high expression
of the latter enzyme, leading up to more rapid drug metabolism, which is seen
more often in the black population. However, many of these same drugs are also
metabolized faster if an individual possesses a particular p-glycoprotein
polymorphism. These are more common among Caucasian individuals. Thus,
customized treatment will have to consider all the polymorphisms that alter a
drug metabolism.
The identification of drugs that may have different efficacies in
different racial groups may lead to questions of discrimination if some drugs are
developed that benefit particular groups, even if no benefit is ensured. Any
approach is complicated by:
1. False negative – where there
are no differences between the tissue used in research and the tissue of action
in the body.
2. False positive – simply
because of the large number of areas that are being looked at, as areas will be
identified by chance alone.
Identified regions in the genome will need to be confirmed through
epidemiological association and biochemical functional studies, as well as in
clinical models. The future hope for pharmacogenomics is the development of:
New drugs.
Genes identified with differing expression in cancer cells that are
sensitive or resistant to anti-cancer drugs are candidates for the development
of inhibitors of the gene product, reversing the drug-resistant phenotype.
Development of drugs, or drug combinations. Targeted to particular
tissues to maximize therapeutic benefits and decrease damage in healthy cells.
Safer and better drugs.
Instead of the current ‘trial and error’ approach, where a patient is
treated and switched to another therapy if the first one does not work or has
too many side effects, knowledge of the patient’s genetic profile may allow the
more appropriate treatment to be given from the start.
Appropriate drug dose.
Genetic response may be a better way to determine dose than a person’s
body mass in future.
Susceptibility to disease
Most diseases are influenced by environmental factors and knowledge of
risk may allow individuals to make important lifestyle changes and influence
the timing of future drug therapies.
Genetic variants associated with increased risk of many common diseases
are being identified.
Better drug discovery.
Many potential useful drugs have been abandoned because of the toxic
side effects in some people. If this can be shown to be linked to polymorphic
variations then individuals can be selected to receive, or not receive the
particular therapy.
For example, abacavir, an anti-HIV drug produces extreme
hypersensitivity reactions in a minority of patients. This has been linked to
possession of the HLA-B*5701 genotype and the prospective screening of the
individuals has led to a significant reduction in side effects of abacavir.
Lower healthcare cost
The cost associated with getting a drug to market will be reduced if there is more information that allows the prediction of the likely response though knowledge of the genetic pathways involved.
Antibiotics and pharmacogenomics
The increasing resistance of bacterial pathogens to the current
antibiotics has led to the need for new ways to identify potential
antimicrobial compounds. Traditional methods of identifying such compounds have
involved whole-cell screening assays, with selections based on antimicrobial
activities. More recently biochemical assays have been used to screen compounds
for their ability to target enzymes or specific cellular pathways. Neither
approach, however, has resulted in many new antibiotics being developed.
A more rational approach in the identification of potential antibiotic targets has come from genomic sequencing. The genomes of more than 100 bacteria have been sequenced and this allows the identification of proteins that are conserved across pathogens. This approach produces better information across pathogens. The approach produces better information about the likely spectrum of activity of an antimicrobial agent against a particular protein and is an unbiased approach. Comparison with the human genome also allows the identification of homologues that could present toxicity problems. Using currently available data, around 300 potential drugs targets have been identified.
Evidence-based treatment.
We are some way off using pharmacogenomic approaches for making
treatment decisions. Despite there being clear candidates for their use.
Current approaches in drug therapy use a trial-and-error approach, starting
with a standard dose that will be modified by the results of biochemical tests
or reporting of side effects. Changes in clinical practice will not come
without proper randomized controlled studies that demonstrate a benefit in
outcome. This will require a significant investment and there may be commercial
pharmaceutical pressures that do not necessarily see the advantage of the
approach. Despite the cost of essential clinical trials, others will point to
the huge cost of providing an individual genetic profile, although this will be
offset by the reduced ongoing need to monitor deleterious effects through
biochemical tests, and cost is already being driven down through the
introduction of SNP genotyping arrays.
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